POS1055 IDENTIFICATION OF PsA PHENOTYPES WITH MACHINE LEARNING ANALYTICS USING DATA FROM A PHASE 3 CLINICAL TRIAL PROGRAMME OF GUSELKUMAB IN A BIO-NAÏVE PATIENT POPULATION
Authors: P. Richette, M. Vis, S. Ohrndorf, W. Tillett, M. Neuhold, M. Van Speybroeck, E. Theander, W. Noel, M. Shawi, A. Kollmeier, A. Zabotti
Journal: Annals of the Rheumatic Diseases (2022)

Background Psoriatic arthritis (PsA) is typically described by its individual domains or clinical components. 1,2 Objectives This post hoc analysis aimed to identify hypothesis-free phenotype clusters according to patients’ clinical features and baseline (BL) characteristics with data from the Phase 3 DISCOVER-1 and -2 guselkumab (GUS) clinical trials. Methods Data from bio-naïve patients with PsA treated with GUS 100 mg every 4 or 8 weeks in DISCOVER-1 and -2 were retrospectively analysed. Non-negative matrix factorisation was used as an unsupervised machine learning technique to identify clusters of PsA phenotypes, with BL characteristics and clinical observations as input features, according to which clusters were described. Results Data from 661 patients were pooled and 8 distinct clusters of PsA phenotypes identified (Table 1). Cluster 1 was characterised by lower limb involvement and the lowest rates of severe skin involvement (Figure 1); Cluster 2 by high skin involvement, the lowest proportion of women and highest proportion of overweight patients (body mass index [BMI] 25–&lt;30, 70%); and Cluster 3 by high burden of disease in the hand/wrist. In Cluster 4 all patients had dactylitis and ≥3% body surface area (BSA) psoriasis involvement and the second highest proportion of men. Cluster 5 had the highest BL enthesitis rate; large joint involvement was also common. Cluster 6 had a high level of small joint involvement in the hands/feet, but low mean dactylitis score; nail involvement and BL enthesitis were also common. In Cluster 7, all patients had axial involvement at BL, 49.4% had dactylitis, 69.9% had enthesitis and most had BSA ≥3% (Figure 1). Cluster 8 had limited joint involvement, extensive skin involvement and the highest proportion of obese patients (BMI &gt;30, 67%). Minimal disease activity (MDA) response rates at Week (W)24 and W52 were highest in Cluster 2 and lowest in Cluster 5. Clusters 3 and 4 had low MDA response rates at W24, increasing at W52. Table 1. Baseline characteristics of PsA phenotype clusters. Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7 Cluster 8 Feet dominant Male, overweight, psoriasis burden Hand dominant Dactylitis dominant Enthesitis and large joints Enthesitis and small joints Axial dominant Female, obese, large joints Randomised and treated patients, n 79 125 95 38 57 60 83 124 Age, years 45.8 (10.4) 45.8 (13.2) 48.9 (11.8) 44.8 (12.3) 43.6 (13.3) 45.8 (11.6) 43.8 (10.4) 47.5 (11.1) Female % 54.4 20.0 60.0 26.3 64.9 41.7 36.1 61.3 BMI, kg/m 2 29.3 (5.4) 27.4 (3.6) 29.3 (6.1) 26.9 (4.6) 28.6 (7.5) 29.0 (6.2) 28.4 (6.9) 32.1 (6.5 ) CRP, mg/dL 1.7 (1.8) 1.7 (2.0) 1.4 (2.3) 2.5 (2.8 ) 1.7 (1.9) 1.7 (1.8) 2.2 (3.1) 1.5 (1.8) Disease duration, years 5.0 (5.3) 5.1 (4.8) 5.8 (6.5) 6.1 (5.5) 6.8 (7.6) 6.1 (5.6) 4.7 (4.6) 5.2 (6.7) SJC, 0–66 13.2 (7.1) 8.2 (3.8) 15.0 (8.0) 18.0 (10.1 ) 10.1 (5.1) 17.5 (11.8 ) 9.0 (4.2) 8.8 (3.9) TJC, 0–68 23.4 (10.1) 12.8 (5.9) 26.0 (12.0) 30.6 (15.3 ) 23.2 (12.6) 37.5 (18.6 ) 14.6 (6.5) 12.7 (5.2) BSA, % 12.6 (19.4) 20.7 (19.8) 14.8 (19.5) 29.7 (26.4) 14.6 (21.6) 14.5 (18.4) 15.2 (19.4) 14.4 (15.7) Dactylitis % 48.1 30.4 42.1 100.0 36.8 53.3 49.4 31.5 Dactylitis score 3.0 (4.8) 1.4 (3.2) 2.7 (4.8) 27.5 (12.3 ) 2.3 (5.2) 3.9 (6.8) 2.2 (2.9 ) 1.3 (2.5) Enthesitis % 70.9 49.6 58.9 81.6 96.5 73.3 69.9 45.2 LEI score 2.0 (1.7) 1.0 (1.3) 1.7 (1.8) 2.9 (1.9) 4.2 (1.6 ) 2.7 (2.3 ) 1.3 (1.2) 1.0 (1.3) Data shown are mean (standard deviation) or %. Bold font indicates differentiating features of individual clusters. BMI, body mass index; BSA, body surface area; CRP, C-reactive protein; LEI, Leeds Enthesitis Index; PsA, psoriatic arthritis; SJC, swollen joint count; TJC, tender joint count. Conclusion Unsupervised machine learning identified 8 clusters of PsA phenotypes with significant differences in demographic and clinical features, including patterns of domain involvement and MDA responses. These clusters differ in their initial vs. later responses to GUS. References [1]Coates C et al. Arthritis Rheumatol 2016; 68: 1060–1071. [2]Gossec L et al. Ann Rheum Dis 2012; 71: 4–12. Disclosure of Interests Pascal Richette Speakers bureau:, Consultant of: Pascal Richette has received fees from AbbVie, Amgen, Celgene, Janssen, Lilly, MSD, Novartis, Pfizer and UCB., Marijn Vis Speakers bureau:, Consultant of:, Grant/research support from: Marijn Vis has received research grants, consulting or speaker fees from AbbVie, Amgen, Eli Lilly, Janssen, Novartis, Pfizer, UCB and the Dutch Arthritis Foundation., Sarah Ohrndorf Speakers bureau: Sarah Ohrndorf has received speaker fees or travel expense reimbursements from AbbVie, BMS, Janssen, Novartis and Pfizer., William Tillett Speakers bureau:, Consultant of:, Grant/research support from: William Tillett has received research grants, consulting or speaker fees from AbbVie, Amgen, Celgene, Eli Lilly, Janssen, MSD, Novartis, Pfizer and UCB., Marlies Neuhold Shareholder of: Johnson &amp; Johnson, Employee of: Janssen, Michel van Speybroeck Shareholder of: Johnson &amp; Johnson, Employee of: Janssen, Elke Theander Shareholder of: Johnson &amp; Johnson, Employee of: Janssen, Wim Noel Shareholder of: Johnson &amp; Johnson, Employee of: Janssen, May Shawi Shareholder of: Johnson &amp; Johnson, Employee of: Janssen, Alexa Kollmeier Shareholder of: Johnson &amp; Johnson, Employee of: Janssen, Alen Zabotti Speakers bureau: AbbVie, Amgen, Eli Lilly, Novartis and UCB, Grant/research support from: Novartis